Task Assignment and Transaction Clustering Heuristics

In this paper, we present and discuss the task assignment problem for distributed system. We present some novel heuristic algorithm that we have tested for solving and compare them to the well-known greedy heuristic. These novel heuristic use genetic algorithm (GA), and simulated annealing (SA).

Problem Definition Goal: This goal is usually represented as some cost function which may consider a combination of several criteria: (1)minimization of the amount (and delay) of communication between the processors. (2)minimization of the execution time of the program.

Simulated Annealing (SA) Simulated Annealing (SA) is a well-known method which uses the physical concepts of “temperature and energy” to represent and solve optimization problems. The idea is to start with an initial solution, and then try to improve it through local change.

Simulated Annealing (SA) The procedure is the following: (1)the system is submitted to high temperature and is then slowly cooled through a series of temperature levels. (2)For each level, we search for the system’s equilibrium state through elementary transformations which will be accepted if they reduce the system energy If the initial temperature is very high, then the execution time of the heuristic becomes very long, and if it is low, then poor results are obtained.

Simulated Annealing (SA) The cooling rate defines the procedure to reduce the temperature: --a rapid reduction yields a bad local optimum. Slow cooling is also expensive in CPU time.

Genetic Algorithm (GA)

Performance comparisons In this section, we summarize the results we have obtained for the heuristics described above, which we compare with each other and with the well-known Kernighan-Lin graph-partitioning heuristic.

Performance comparisons

GA SA Kern